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dc.contributor.authorIvkovic, Milos
dc.contributor.authorChilappagari, Shashi Kiran
dc.contributor.authorVasic, Bane
dc.date.accessioned2020-07-31T23:25:47Z
dc.date.available2020-07-31T23:25:47Z
dc.date.issued2008-08-01
dc.identifier.citationM. Ivkovic, S. K. Chilappagari and B. Vasic, "Eliminating trapping sets in low-density parity-check codes by using Tanner graph covers," in IEEE Transactions on Information Theory, vol. 54, no. 8, pp. 3763-3768, Aug. 2008, doi: 10.1109/TIT.2008.926319.en_US
dc.identifier.issn0018-9448
dc.identifier.doi10.1109/tit.2008.926319
dc.identifier.urihttp://hdl.handle.net/10150/641961
dc.description.abstractWe discuss error floor asympotics and present a method for improving the performance of low-density parity-check (LDPC) codes in the high SNR (error floor) region. The method is based on Tanner graph covers that do not have trapping sets from the original code. The advantages of the method are that it is universal, as it can be applied to any LDPC code/channel/decoding algorithm and it improves performance at the expense of increasing the code length, without losing the code regularity, without changing the decoding algorithm, and, under certain conditions, without lowering the code rate. The proposed method can be modified to construct convolutional LDPC codes also. The method is illustrated by modifying Tanner, MacKay and Margulis codes to improve performance on the binary symmetric channel (BSC) under the Gallager B decoding algorithm. Decoding results on AWGN channel are also presented to illustrate that optimizing codes for one channel/decoding algorithm can lead to performance improvement on other channels.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rightsCopyright © 2008 IEEE.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.titleEliminating trapping sets in low-density parity-check codes by using Tanner graph coversen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Elect & Comp Engnen_US
dc.identifier.journalIEEE Transactions on Information Theoryen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleIEEE Transactions on Information Theory
dc.source.volume54
dc.source.issue8
dc.source.beginpage3763
dc.source.endpage3768
refterms.dateFOA2020-07-31T23:25:48Z


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